The Importance of Sleep Fragmentation on the Hemodynamic Dipping in Obstructive Sleep Apnea Patients - Staats, Richard; Barros, Inês; Fernandes, Dina; Grencho, Dina; Reis, Cátia; Matos, Filipa; Valença, João; Marôco, João; Almeida, António Bugalho de; Bárbara, Cristina (Frontiers in Physiology, 2020)
Introduction: Obstructive sleep apnea (OSA) has been associated with non-dipping blood pressure (BP). The precise mechanism is still under investigation, but repetitive oxygen desaturation and arousal induced sleep fragmentation are considered the main contributors. Methods: We analysed beat-to-beat measurements of hemodynamic parameters (HP) during a 25 minute period of wake-sleep transition. Differences in the mean HP values for heart rate (HR), systolic blood pressure (SBP) and stroke volume (SV) during wake and sleep and their standard deviations (SD) were compared between 34 controls (C) and 22 OSA patients. The student t-test for independent samples and the effect size by Cohen´s d (d) was calculated. HP evolution was investigated by plotting the measured HP values against each consecutive pulse wave. After a simple regression analysis, the calculated coefficient beta (SCB) was used to indicate the HP evolution. We furthermore explored by a hierarchical block regression which variables increased the prediction for the SCB: model 1 BMI and age, model 2 + apnea/hypopnea index (AHI) and model 3 + arousal index (AI). Results: Between the two groups the SBP increased in OSA and decreased in C resulting in a significant difference (p=0.001; d=0.92). The SV demonstrated a similar development (p=0.047; d=0.56). The wake/sleep variation of the HP measured by the SD was higher in the OSA group: HR: p<0.001; d=1.2; SBP: p= 0.001; d=0.94 and SV: p=0.005; d=0.82. The hierarchical regression analysis of the SCB demonstrated in SBP that the addition of AI to AHI resulted in: ΔR2 : + 0.163. and ΔF +13.257 (p=0.001) and for SV ΔR 2: + 0.07 and ΔF 4.83 (p=0.003). The AI but not the AHI remained statistically significant in the regression analysis model 3: SBP: β= 0.717; p=0.001) SV: β= 34 0.469; p=0.033). Conclusion: In this study we demonstrated, that in OSA the physiological dipping in SBP and SV decreased, and the variation of all investigated parameters increased. Hierarchical regression analysis indicates that the addition of the AI to BMI, age and AHI, increases the prediction of the HP evolution following sleep onset for both SBP and SV and may be the most important variable.